SoroushVahidi/scidocs huggingface dataset:
Overview of High-Step-Up Coupled-Inductor Boost Converters
(2016)

High-step-up, high-efficiency, and cost-effective dc-dc converters, serving as an interfacing cell to boost the low-voltage output of renewable sources to the utility voltage level, are an important part in renewable energy systems. Over the past few years, there has been a substantial amount of studies devoted to high-step-up dc-dc converters. Among them, the category of coupled-inductor boost converters is widely researched and considered to be a promising solution for high-step-up applications. In this paper, these converters are categorized into five groups according to the major topological features. The derivation process, advantages, and disadvantages of these converters are systematically discussed, compared, and scrutinized. This paper aims to provide an introduction, review, and framework for the category of high-step-up coupled-inductor boost converters. General structures for the topologies are proposed to clarify the topological derivation process and to show potential gaps. Furthermore, challenges or directions are presented in this paper for deriving new topologies in this field.
Design and Analysis of a Two Stage Operational Amplifier for High Gain and High Bandwidth
(2012)

In this paper a design and comparison between a fully differential RC Miller compensated CMOS op-amp and conventional op-amp is presented. High gain enables the circuit to operate efficiently in a closed loop feedback system, whereas high bandwidth makes it suitable for high speed applications. A novel RC Miller compensation technique is used to optimize the parameters of gain and bandwidth for high speed applications are illustrated in this research work. The design is also able to address any fluctuation in supply or dc input voltages and stabilizes the operation by nullifying. The design is implemented on TSMC 0.18 m CMOS process at 3.3 V as supply voltage under room temperature 27 C. The simulated result shows that a unity gain bandwidth of 136.8 MHz with a high gain of 92.27 dB is achieved for the proposed op-amp circuit. The total areas of the layouts are 0.000158 mm and 0.000532 mm for conventional and proposed respectively. Key wrods: Two-Stage Op-amp, fully differential, high gain, high bandwidth
Assembler: Efficient Discovery of Spatial Co-evolving Patterns in Massive Geo-sensory Data
(2015)

Recent years have witnessed the wide proliferation of geo-sensory applications wherein a bundle of sensors are deployed at different locations to cooperatively monitor the target condition. Given massive geo-sensory data, we study the problem of mining spatial co-evolving patterns (SCPs), i.e., groups of sensors that are spatially correlated and co-evolve frequently in their readings. SCP mining is of great importance to various real-world applications, yet it is challenging because (1) the truly interesting evolutions are often flooded by numerous trivial fluctuations in the geo-sensory time series; and (2) the pattern search space is extremely large due to the spatiotemporal combinatorial nature of SCP. In this paper, we propose a two-stage method called Assember. In the first stage, Assember filters trivial fluctuations using wavelet transform and detects frequent evolutions for individual sensors via a segment-and-group approach. In the second stage, Assember generates SCPs by assembling the frequent evolutions of individual sensors. Leveraging the spatial constraint, it conceptually organizes all the SCPs into a novel structure called the SCP search tree, which facilitates the effective pruning of the search space to generate SCPs efficiently. Our experiments on both real and synthetic data sets show that Assember is effective, efficient, and scalable.
A Markov Model for Headway/Spacing Distribution of Road Traffic
(2010)

In this paper, we link two research directions of road traffic-the mesoscopic headway distribution model and the microscopic vehicle interaction model-together to account for the empirical headway/spacing distributions. A unified car-following model is proposed to simulate different driving scenarios, including traffic on highways and at intersections. Unlike our previous approaches, the parameters of this model are directly estimated from the Next Generation Simulation (NGSIM) Trajectory Data. In this model, empirical headway/spacing distributions are viewed as the outcomes of stochastic car-following behaviors and the reflections of the unconscious and inaccurate perceptions of space and/or time intervals that people may have. This explanation can be viewed as a natural extension of the well-known psychological car-following model (the action point model). Furthermore, the fast simulation speed of this model will benefit transportation planning and surrogate testing of traffic signals.
Design of a Marx Generator for HEMP filter evaluation taking account of parasitic effect of components
(2016)

This paper introduces the design of a Marx Generator that can generate high attitude pulses up to 25 kV and 2.5 kA. The pulses have a wave shape representative of that used to test High-altitude ElectroMagnetic (HEMP) filters against the pulse current injection (PCI) requirements. The rise time is 20 ns and the pulse width is 230 ns on a 10 Ω resistor. In the design, it was found that parasitic effect of circuit components could significantly affect the performance of the generator. This is mainly because the pulse width is very narrow and its frequency spectrum is very wideband. The high-frequency circuit models of the components used in the generator were obtained by calculation and optimization, and then further verified by experiment. The verified circuit models were then used to design the three-stage generator. The measured performance agrees very well with the simulated one using the proposed circuit models.
Brain activation during human male ejaculation.
(2003)

Brain mechanisms that control human sexual behavior in general, and ejaculation in particular, are poorly understood. We used positron emission tomography to measure increases in regional cerebral blood flow (rCBF) during ejaculation compared with sexual stimulation in heterosexual male volunteers. Manual penile stimulation was performed by the volunteer's female partner. Primary activation was found in the mesodiencephalic transition zone, including the ventral tegmental area, which is involved in a wide variety of rewarding behaviors. Parallels are drawn between ejaculation and heroin rush. Other activated mesodiencephalic structures are the midbrain lateral central tegmental field, zona incerta, subparafascicular nucleus, and the ventroposterior, midline, and intralaminar thalamic nuclei. Increased activation was also present in the lateral putamen and adjoining parts of the claustrum. Neocortical activity was only found in Brodmann areas 7/40, 18, 21, 23, and 47, exclusively on the right side. On the basis of studies in rodents, the medial preoptic area, bed nucleus of the stria terminalis, and amygdala are thought to be involved in ejaculation, but increased rCBF was not found in any of these regions. Conversely, in the amygdala and adjacent entorhinal cortex, a decrease in activation was observed. Remarkably strong rCBF increases were observed in the cerebellum. These findings corroborate the recent notion that the cerebellum plays an important role in emotional processing. The present study for the first time provides insight into which regions in the human brain play a primary role in ejaculation, and the results might have important implications for our understanding of how human ejaculation is brought about, and for our ability to improve sexual function and satisfaction in men.
Data Center Energy Consumption Modeling: A Survey
(2016)

Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.
Differential quasi self-complimentary (QSC) ultra-wideband (UWB) MIMO antenna
(2017)

In this paper, a differentially excited ultra-wideband (UWB) MIMO antenna is designed for pattern diversity applications, utilizing quasi self-complimentary (QSC) antenna elements. Four QSC elements are realized using half- octagon shaped monopoles, and their complementary cuts from the ground plane. Due to the QSC property, wide impedance bandwidth covering the UWB frequency range of 3–11 GHz is achieved within very less antenna footprint. The QSC elements are placed symmetrically about the four edges of a square substrate. Two oppositely positioned elements are fed with 180o phase difference, in order to form a single differential pair. Two such differential pairs are placed perpendicular to each other for realizing pattern diversity performance with high value of differential isolation.
Airway management and smoke inhalation injury in the burn patient.
(2009)

Smoke inhalation injury, a unique form of acute lung injury, greatly increases the occurrence of postburn morbidity and mortality. In addition to early intubation for upper-airway protection, subsequent critical care of patients who have this injury should be directed at maintaining distal airway patency. High-frequency ventilation, inhaled heparin, and aggressive pulmonary toilet are among the therapies available. Even so, immunosuppression, intubation, and airway damage predispose these patients to pneumonia and other complications.
Tibiofemoral movement 1: the shapes and relative movements of the femur and tibia in the unloaded cadaver knee.
(2000)

In six unloaded cadaver knees we used MRI to determine the shapes of the articular surfaces and their relative movements. These were confirmed by dissection. Medially, the femoral condyle in sagittal section is composed of the arcs of two circles and that of the tibia of two angled flats. The anterior facets articulate in extension. At about 20 degrees the femur 'rocks' to articulate through the posterior facets. The medial femoral condyle does not move anteroposteriorly with flexion to 110 degrees. Laterally, the femoral condyle is composed entirely, or almost entirely, of a single circular facet similar in radius and arc to the posterior medial facet. The tibia is roughly flat. The femur tends to roll backwards with flexion. The combination during flexion of no anteroposterior movement medially (i.e., sliding) and backward rolling (combined with sliding) laterally equates to internal rotation of the tibia around a medial axis with flexion. About 5 degrees of this rotation may be obligatory from 0 degrees to 10 degrees flexion; thereafter little rotation occurs to at least 45 degrees. Total rotation at 110 degrees is about 20 degrees, most if not all of which can be suppressed by applying external rotation to the tibia at 90 degrees.
A Layered Security Approach for Cloud Computing Infrastructure
(2009)

This paper introduces a practical security model based on key security considerations by looking at a number of infrastructure aspects of Cloud Computing such as SaaS, Utility, Web, Platform and Managed Services, Service commerce platforms and Internet Integration which was introduced with a concise literature review. The purpose of this paper is to offer a macro level solution for identified common infrastructure security requirements. This model with a number of emerged patterns can be applied to infrastructure aspect of Cloud Computing as a proposed shared security approach in system development life cycle focusing on the plan-built-run scope.
Development of a three-dimensional ball rotation sensing system using optical mouse sensors
(2011)

Robots using ball(s) as spherical wheels have the advantage of omnidirectional motion. Some of these robots use only a single ball as their wheel and dynamically balance on it. However, driving a ball wheel is not straightforward. These robots usually use one or more motor-driven rollers or wheels in frictional contact with the ball wheel. Some slippage can occur with these schemes, so it is important to measure the actual rotation of the ball wheel for good control. This paper proposes one method for measuring the three dimensional rotation of a sphere, which is applicable to the case of a ball wheel. The system measures surface speed by using two or more optical mouse sensors and transforms them into the angular velocity vector of the ball, followed by integration to give the rotational angle. Experiments showed the correctness of this method, yielding an error of approximately 1%, with a 10 ms response.
IMPLEMNTATION OF SIMULINK BASED MODEL USING SOBEL EDGE DETECTOR FOR DENTAL PROBLEMS Deepika Nagpal
(2013)

Image Segmentation is the process of partitioning a digital image into multiple regions or sets of pixels.Edge Detection is one of the main Technique used in Segmentation.In this paper we used Sobel edge detector for segmenting the dental X-ray image.Using MATLAB,Image is segmented.Sysytem Test tool is used for the verification of the Simulink Model. The Simulink Model based Image Segmentation is a new function in image processing and offers a model based design for processing. Dental Caries is the main problem occurred in the teeths.Segmentation help to identify the places where the problems of dental caries are present.
Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages
(2015)

Early diagnosis of Alzheimer disease (AD), while still at the stage known as mild cognitive impairment (MCI), is important for the development of new treatments. However, brain degeneration in MCI evolves with time and differs from patient to patient, making early diagnosis a very challenging task. Despite these difficulties, many machine learning techniques have already been used for the diagnosis of MCI and for predicting MCI to AD conversion, but the MCI group used in previous works is usually very heterogeneous containing subjects at different stages. The goal of this paper is to investigate how the disease stage impacts on the ability of machine learning methodologies to predict conversion. After identifying the converters and estimating the time of conversion (TC) (using neuropsychological test scores), we devised 5 subgroups of MCI converters (MCI-C) based on their temporal distance to the conversion instant (0, 6, 12, 18 and 24 months before conversion). Next, we used the FDG-PET images of these subgroups and trained classifiers to distinguish between the MCI-C at different stages and stable non-converters (MCI-NC). Our results show that MCI to AD conversion can be predicted as early as 24 months prior to conversion and that the discriminative power of the machine learning methods decreases with the increasing temporal distance to the TC, as expected. These findings were consistent for all the tested classifiers. Our results also show that this decrease arises from a reduction in the information contained in the regions used for classification and by a decrease in the stability of the automatic selection procedure.
Graph Partitioning using Quantum Annealing on the D-Wave System
(2017)

Graph partitioning (GP) applications are ubiquitous throughout mathematics, computer science, chemistry, physics, bio-science, machine learning, and complex systems. Post Moore's era supercomputing has provided us an opportunity to explore new approaches for traditional graph algorithms on quantum computing architectures. In this work, we explore graph partitioning using quantum annealing on the D-Wave 2X machine. Motivated by a recently proposed graph-based electronic structure theory applied to quantum molecular dynamics (QMD) simulations, graph partitioning is used for reducing the calculation of the density matrix into smaller subsystems rendering the calculation more computationally efficient. Unconstrained graph partitioning as community clustering based on the modularity metric can be naturally mapped into the Hamiltonian of the quantum annealer. On the other hand, when constraints are imposed for partitioning into equal parts and minimizing the number of cut edges between parts, a quadratic unconstrained binary optimization (QUBO) reformulation is required. This reformulation may employ the graph complement to fit the problem in the Chimera graph of the quantum annealer. Partitioning into 2 parts and k parts concurrently for arbitrary k are demonstrated with benchmark graphs, random graphs, and small material system density matrix based graphs. Results for graph partitioning using quantum and hybrid classical-quantum approaches are shown to be comparable to current "state of the art" methods and sometimes better.
Typology of Distributed Ledger based Business Models
(2017)

The potential of distributed ledger technology and its application in various industries is a controversially debated topic. Advocates of the technology emphasize the economic benefits of decentralization and transparency, leading to cost reductions as well as the alleviation of several of today`s economic and technological problems. In contrast, critics assert that the potential of distributed ledgers might be overhyped, possibly leading to the next tech bubble. This paper contributes to the discussion by developing a typology of business models that are based on distributed ledger technology. In particular, this paper is a first step towards a more differentiated discussion on the potential of distributed ledges, by taking the underlying business models into consideration. Despite a characterization of the types, a discussion about special features of distributed ledger based business models is provided in the context of contemporary business model literature and the associated role of IT. It is proposed that future research must evaluate each business model isolated to achieve a comprehensive assessment of the potential of distributed ledgers. This paper can be interpreted as starting point for more fruitful discussions and the repeal of the partially diametrical opposed opinions towards the potentials of the technology.
Learning structural SVMs with latent variables
(2009)

We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of application problems, with an optimization problem that can be solved efficiently using Concave-Convex Programming. The generality and performance of the approach is demonstrated through three applications including motiffinding, noun-phrase coreference resolution, and optimizing precision at k in information retrieval.
Transferring a semantic representation for person re-identification and search
(2015)

Learning semantic attributes for person re-identification and description-based person search has gained increasing interest due to attributes' great potential as a pose and view-invariant representation. However, existing attribute-centric approaches have thus far underperformed state-of-the-art conventional approaches. This is due to their nonscalable need for extensive domain (camera) specific annotation. In this paper we present a new semantic attribute learning approach for person re-identification and search. Our model is trained on existing fashion photography datasets - either weakly or strongly labelled. It can then be transferred and adapted to provide a powerful semantic description of surveillance person detections, without requiring any surveillance domain supervision. The resulting representation is useful for both unsupervised and supervised person re-identification, achieving state-of-the-art and near state-of-the-art performance respectively. Furthermore, as a semantic representation it allows description-based person search to be integrated within the same framework.
Supervised Descent Method and Its Applications to Face Alignment
(2013)

Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved through a nonlinear optimization method. It is generally accepted that 2nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, 2nd order descent methods have two main drawbacks: (1) The function might not be analytically differentiable and numerical approximations are impractical. (2) The Hessian might be large and not positive definite. To address these issues, this paper proposes a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent directions without computing the Jacobian nor the Hessian. We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-of-the-art performance in the problem of facial feature detection. The code is available at www.humansensing.cs. cmu.edu/intraface.
An Optimal Algorithm for Generalized Causal Message Ordering (Abstract)
(1996)

Asynchronous execution of processes and unpredictable communication delays create nondeterminism in distributed systems that complicates the design, verification, and analysis of distributed programs. To simplify the design and development of distributed applications, the idea of “causal message ordering” (CO) was introduced by Birman and Joseph [1]. If for any two messages M and M’ sent to the same destination d, Send(M) + Send(M’), then CO ensures that Delivered + Deliver-yd(lf’) at d. Existing algorithms that provide CO have high message overhead and use more storage than is required by an optimal algorithm, or make simplifying assumptions by assuming certain topolog y/communicat ion pat t ems and inbuilt synchronize ation. We present an optimal CO algorithm [2] under the following framework: